class_docstr: Concept Activation Vector (CAV) is a vector orthogonal to the decision
  boundary of a classifier which distinguishes between activation
  vectors produced by different concepts.
  More details can be found in the paper:
      https://arxiv.org/abs/1711.11279
docstr: This class encapsulates the instances of CAVs objects, saves them in
  and loads them from the disk (storage).
  
  Args:
      concepts (list[Concept]): a List of Concept objects. Only their
                  names will be saved and loaded.
      layer (str): The layer where concept activation vectors are
                  computed using a predefined classifier.
      stats (dict, optional): a dictionary that retains information about
                  the CAV classifier such as CAV weights and accuracies.
                  Ex.: stats = {"weights": weights, "classes": classes,
                                "accs": accs}, where "weights" are learned
                  model parameters, "classes" are a list of classes used
                  by the model to generate the "weights" and "accs"
                  the classifier training or validation accuracy.
      save_path (str, optional): The path where the CAV objects are stored.
      model_id (str, optional): A unique model identifier associated with
                  this CAV instance.
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code: def __init__(
      self,
      concepts: List[Concept],
      layer: str,
      stats: Dict[str, Any] = None,
      save_path: str = "./cav/",
      model_id: str = "default_model_id",
  ) -> None:
      r"""
      This class encapsulates the instances of CAVs objects, saves them in
      and loads them from the disk (storage).
  
      Args:
          concepts (list[Concept]): a List of Concept objects. Only their
                      names will be saved and loaded.
          layer (str): The layer where concept activation vectors are
                      computed using a predefined classifier.
          stats (dict, optional): a dictionary that retains information about
                      the CAV classifier such as CAV weights and accuracies.
                      Ex.: stats = {"weights": weights, "classes": classes,
                                    "accs": accs}, where "weights" are learned
                      model parameters, "classes" are a list of classes used
                      by the model to generate the "weights" and "accs"
                      the classifier training or validation accuracy.
          save_path (str, optional): The path where the CAV objects are stored.
          model_id (str, optional): A unique model identifier associated with
                      this CAV instance.
      """
  
      self.concepts = concepts
      self.layer = layer
      self.stats = stats
      self.save_path = save_path
      self.model_id = model_id
  
file_path: captum/captum/concept/_core/cav.py
imports: import os
  from typing import Any, Dict, List
  import torch
  from captum.concept._core.concept import Concept
  from captum.concept._utils.common import concepts_to_str
label_desc: Use image node_id 1 to create a new CAV object  with example: obj = CAV(concepts, layer, stats, save_path, model_id)